SPIN vs. VIS: a case study on the formal verification of the ATMR protocol
Bibliographic record
Abstract
Nowadays, there exist a wide variety of verification tools. Some, like the SPIN model checker, are designed and mainly used for the verification of interleaving software systems, such as communication protocols. Others, like VIS (Verification Interacting with Synthesis), are designed and used for synchronous hardware systems verification. In this paper, we compare and contrast SPIN and VIS. In particular, we devote a special attention to the efficiency of these tools for the verification of communications protocols that can be implemented either in software or hardware. As a basis of our comparison, we formally describe and verify the ATMR (Asynchronous Transfer Mode Ring) medium access protocol using SPIN, and its hardware implementation using VIS. We believe that this study is of particular interest, as more and more protocols, like the ATM protocol stack, are being implemented in hardware in order to match high speed requirements. However, this is not a formal comparison of SPIN and VIS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".